Find Vector DatabasesYou Can Actually Trust

Vector database software stores, indexes, and queries high-dimensional vector embeddings for AI applications including semantic search, recommendation systems, and retrieval-augmented generation (RAG). These specialized databases handle the unique requirements of similarity search across millions to billions of vectors with sub-second query latency. Approximate nearest neighbor (ANN) algorithms like HNSW, IVF, and product quantization enable fast similarity search without exhaustively comparing every vector. Metadata filtering combines vector similarity with attribute-based filtering for hybrid queries like "find similar products under $50." Multi-tenancy support isolates data between customers or applications within a single deployment. Real-time indexing ingests new vectors and makes them searchable immediately without batch reindexing. Scalability features handle growing datasets through sharding, replication, and distributed architectures. Integration with embedding models (OpenAI, Cohere, Hugging Face) and AI frameworks (LangChain, LlamaIndex) simplifies the pipeline from raw data to searchable vectors.